Authentication method and apparatus with transformation model

ABSTRACT

An authentication method and apparatus using a transformation model are disclosed. The authentication method includes generating, at a first apparatus, a first enrolled feature based on a first feature extractor, obtaining a second enrolled feature to which the first enrolled feature is transformed, determining an input feature by extracting a feature from input data with a second feature extractor different from the first feature extractor, and performing an authentication based on the second enrolled feature and the input feature.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2019-0030147 filed on Mar. 15, 2019 in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to an authentication method andapparatus with a transformation model.

2. Description of Related Art

Technological automation of speech recognition has been implementedthrough processor implemented neural network models, as specializedcomputational architectures, that after substantial training may providecomputationally intuitive mappings between input patterns and outputpatterns. The trained capability of generating such mappings may bereferred to as a learning capability of the neural network. Further,because of the specialized training, such specially trained neuralnetwork may thereby have a generalization that generates a relativelyaccurate output with respect to an input pattern that the neural networkmay not have been trained for, for example.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In a general aspect, a processor implemented authentication methodincludes obtaining a second enrolled feature to which a first enrolledfeature generated based on a first feature extractor is transformed;determining an input feature by extracting a feature from input datawith a second feature extractor different from the first featureextractor; and performing an authentication based on the second enrolledfeature and the input feature.

The obtaining may include transforming the first enrolled feature to thesecond enrolled feature with a transformation model.

The obtaining may include receiving, from another apparatus, the secondenrolled feature to which the first enrolled feature is transformed.

The second feature extractor may be an updated version of the firstfeature extractor.

The second enrolled feature may be obtained based on a transformationmodel.

The transformation model may include a structural element thatcorresponds to a difference between a structure of the first featureextractor and a structure of the second feature extractor.

The first feature extractor may be pretrained to output first outputdata in response to an input of first input data, the second featureextractor is pretrained to output second output data in response to aninput of the first input data, and the transformation model ispretrained to output the second output data in response to an input ofthe first output data.

The first enrolled feature may include first sub-enrolled features, andthe second enrolled feature includes second sub-enrolled features towhich the first sub-enrolled features are transformed.

The method may include discarding at least a portion of the secondsub-enrolled features based on suitabilities of the second sub-enrolledfeatures.

The method may include discarding at least a portion of the secondsub-enrolled features based on a similarity between the secondsub-enrolled features.

The discarding may include discarding at least one of the secondsub-enrolled features based on a second threshold and similaritiesbetween the at least one second sub-enrolled feature and remainingsecond sub-enrolled features.

The performing may include performing the authentication based on afirst threshold and a similarity between the second enrolled feature andthe input feature, wherein the first threshold is equal to the secondthreshold.

In a general aspect, an authentication apparatus includes one or moreprocessors configured to obtain a second enrolled feature to which afirst enrolled feature is transformed; determine an input feature byextracting a feature from input data with a second feature extractordifferent from the first feature extractor; and perform anauthentication based on the second enrolled feature and the inputfeature.

The second enrolled feature may be obtained based on a transformationmodel.

The transformation model may include a structural element thatcorresponds to a difference between a structure of the first featureextractor and a structure of the second feature extractor.

The first feature extractor may be pretrained to output first outputdata in response to an input of first input data, the second featureextractor is pretrained to output second output data in response to aninput of the first input data, and the transformation model ispretrained to output the second output data in response to an input ofthe first output data.

The first enrolled feature may include first sub-enrolled features, andthe second enrolled feature includes second sub-enrolled features towhich the first sub-enrolled features are transformed.

The one or more processors may be configured to discard at least aportion of the second sub-enrolled features based on a similaritybetween the second sub-enrolled features.

The one or more processors may be configured to discard one of thesecond sub-enrolled features based on a threshold and similaritiesbetween the one second sub-enrolled feature and the remaining secondsub-enrolled features.

The apparatus may further include a memory storing instructions that,when executed by the one or more processors, configure the one or moreprocessors to perform the generating of the first enrolled feature, theobtaining of the second enrolled feature, the determining of the inputfeature, and the performing of the authentication.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of an enrollment process and anauthentication process in accordance with one or more embodiments.

FIG. 2 illustrates an example of an authentication process with atransformation model in accordance with one or more embodiments.

FIG. 3 illustrates an example of an operation of a transformation modelin accordance with one or more embodiments.

FIG. 4 illustrates an example of generating a transformation model inaccordance with one or more embodiments.

FIG. 5 illustrates an example of generating a transformation model inaccordance with one or more embodiments.

FIG. 6 illustrates an example of second sub-enrolled features inaccordance with one or more embodiments.

FIG. 7 illustrates an example of a transformation operation of a userterminal in accordance with one or more embodiments.

FIG. 8 illustrates an example of a transformation operation through aserver in accordance with one or more embodiments.

FIG. 9 illustrates an example of a transformation operation through ahub device in an Internet of things (IoT) system in accordance with oneor more embodiments.

FIG. 10 illustrates an example of a transformation operation through ahub device in an IoT system in accordance with one or more embodiments.

FIG. 11 illustrates an example of a configuration of an authenticationapparatus in accordance with one or more embodiments.

FIG. 12 illustrates an example of an authentication method in accordancewith one or more embodiments.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The terminology used herein is for describing various examples only, andis not to be used to limit the disclosure. The articles “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. The terms “comprises,” “includes,”and “has” specify the presence of stated features, numbers, operations,members, elements, and/or combinations thereof, but do not preclude thepresence or addition of one or more other features, numbers, operations,members, elements, and/or combinations thereof.

Throughout the specification, when an element, such as a layer, region,or substrate, is described as being “on,” “connected to,” or “coupledto” another element, it may be directly “on,” “connected to,” or“coupled to” the other element, or there may be one or more otherelements intervening therebetween. In contrast, when an element isdescribed as being “directly on,” “directly connected to,” or “directlycoupled to” another element, there can be no other elements interveningtherebetween.

As used herein, the term “and/or” includes any one and any combinationof any two or more of the associated listed items.

Although terms such as “first,” “second,” and “third” may be used hereinto describe various members, components, regions, layers, or sections,these members, components, regions, layers, or sections are not to belimited by these terms. Rather, these terms are only used to distinguishone member, component, region, layer, or section from another member,component, region, layer, or section. Thus, a first member, component,region, layer, or section referred to in examples described herein mayalso be referred to as a second member, component, region, layer, orsection without departing from the teachings of the examples.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains after anunderstanding of the disclosure of this application. Terms, such asthose defined in commonly used dictionaries, are to be interpreted ashaving a meaning that is consistent with their meaning in the context ofthe relevant art and the disclosure of the present application, and arenot to be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

FIG. 1 illustrates an example of an enrollment process and anauthentication process, in accordance with one or more embodiments.

Referring to FIG. 1, an authentication apparatus 100 receives input dataand generates output data by processing the received input data. Theinput data may correspond to an input voice or speech, or an inputimage, but is not limited thereto. For example, in an example of speechrecognition, the input data may include voice or audio data. In anexample of facial recognition, the input data may include a facialimage. In an example of fingerprint recognition, the input data mayinclude a fingerprint image. In an example of iris recognition, theinput data may include an iris image. The output data may include anauthentication result. For example, the authentication result mayindicate an authentication success or an authentication failure. Herein,it is noted that use of the term ‘may’ with respect to an example orembodiment, e.g., as to what an example or embodiment may include orimplement, means that at least one example or embodiment exists wheresuch a feature is included or implemented while all examples andembodiments are not limited thereto.

Referring again to FIG. 1, the authentication apparatus 100 performs anenrollment process 151 and an authentication process 152. The enrollmentprocess 151 is a process where a legitimate user enrolls or registershimself or herself in the authentication apparatus 100, and theauthentication process 152 is a process in which a test user attempts anauthentication by incorrectly claiming that he or she is a legitimateuser.

In a non-limited example, the enrollment process 151 may be performedbefore the authentication process 152. According to the enrollmentprocess 151, information pertaining to the legitimate user is enrolledor registered in the authentication apparatus 100. The information ofthe legitimate user enrolled in the authentication apparatus 100 isreferred to as enrolled information or an enrolled feature. It may beexpressed that the enrolled information is “pre”enrolled. Here, “pre”means before the authentication process 152. According to theauthentication process 152, an authentication is deemed to be successfulwhen the test user corresponds to a legitimate user, and theauthentication is deemed to fail when the test user does not correspondto a legitimate user.

In the enrollment process 151, the authentication apparatus 100 extractsat least one feature from the input data using a feature extractor 110,and creates an enrolled feature based on the extracted at least onefeature. The authentication apparatus 100 stores the enrolled feature asinformation of the legitimate user.

In the authentication process 152, the authentication apparatus 100extracts at least one feature from the input data using the featureextractor 110, and determines an input feature based on the extracted atleast one feature. The authentication apparatus 100 compares theenrolled feature and the input feature using a comparator 120, andgenerates an authentication result based on a comparison result. Forexample, the comparison result may include a similarity or a differencebetween the enrolled feature and the input feature, and theauthentication apparatus 100 may generate the authentication result bycomparing the similarity or the difference between the enrolled featureand the input feature, to a threshold, hereinafter identified as TH1.

The feature extractor 110 and the comparator 120 are each implementedthrough at least one hardware module, at least one software module, or acombination thereof. For example, the feature extractor 110 and thecomparator 120 are each implemented as a neural network. In thisexample, at least a portion of the neural network is implemented assoftware, hardware including a neural processor, or a combinationthereof. Technological automation of pattern recognition or analyses,for example, has been implemented through processor implemented neuralnetwork models, as specialized computational architectures, that aftersubstantial training may provide computationally intuitive mappingsbetween input patterns and output patterns or pattern recognitions ofinput patterns. The trained capability of generating such mappings orperforming such pattern recognitions may be referred to as a learningcapability of the neural network. Such trained capabilities may alsoenable the specialized computational architecture to classify such aninput pattern, or portion of the input pattern, as a member that belongsto one or more predetermined groups. Further, because of the specializedtraining, such specially trained neural network may thereby have ageneralization capability of generating a relatively accurate orreliable output with respect to an input pattern that the neural networkmay not have been trained for, for example.

In an example, the neural network may correspond to a deep neuralnetwork (DNN) including a fully connected network, a deep convolutionalnetwork, and a recurrent neural network, or may include different oroverlapping neural network portions respectively with such full,convolutional, or recurrent connections, according to an algorithm usedto process information. The DNN includes a plurality of layers. Theplurality of layers includes an input layer, at least one hidden layer,and an output layer. For example, the DNN may include an input layer towhich input data is applied, an output layer for outputting a resultderived through prediction based on training and the input data, and aplurality of hidden layers for performing a neural network operationbetween the input layer and the output layer.

The neural network may be configured to perform as non-limitingexamples, object classification, object recognition, voice recognition,and image recognition by mutually mapping input data and output datahaving a nonlinear relationship based on deep learning. Such deeplearning is indicative of a processor implemented machine learningscheme for solving issues, such as issues related to automated image orspeech recognition from a big data set, as non-limiting examples. Deeplearning is construed as an optimization problem solving process offinding a point at which energy is minimized while training a neuralnetwork using prepared training data. Through supervised or unsupervisedlearning of deep learning, a structure of the neural network or a weightcorresponding to a model is obtained, and the input data and the outputdata are mapped to each other through the weight.

The neural network is trained based on the training data in a trainingoperation, and performs an inference operation such as classification,recognition, or detection related to the input data in an inferenceoperation. It may be expressed that the neural network is “pre”trained.Here, “pre” means before input data for inference is input into theneural network. That the neural network “starts” means that the neuralnetwork is ready for inference. For example, that the neural network“starts” includes that the neural network is loaded into a memory, orthat input data for inference is input into the neural network after theneural network is loaded into the memory.

The feature extractor 110 generates a feature vector by extracting atleast one feature from the input data. The feature extractor 110 mayinclude a neural network that is pretrained to extract features from anetwork input. The comparator 120 outputs a comparison result includinga similarity or a difference between an input feature vector and anenrolled feature vector. The comparator 120 may include a neural networkthat is pretrained to output a result of the comparison between theinput to the network and the enrolled feature vector.

In the event that a model change or an update related to theauthentication apparatus 100 is made, the feature extractor 110 may bechanged to another feature extractor. Hereinafter, the feature extractor110 is referred to as an old feature extractor or a first featureextractor, and the changed feature extractor that is different from thefeature extractor 110 is referred to as a new feature extractor or asecond feature extractor. For example, the new feature extractor may bean updated version of the old feature extractor. The old featureextractor and the new feature extractor may extract features usingdifferent schemes. In this example, it may be difficult to perform theauthentication process 152 using an enrolled feature generated throughthe old feature extractor and an input feature generated through thesecond feature extractor.

To perform the authentication process 152 based on the new featureextractor, an enrolled feature corresponding to the new featureextractor should be provided. However, the enrollment of a new enrolledfeature using the new feature extractor may be inconvenient for users.

In an example, an enrolled feature corresponding to an old or previousenrolled feature extractor is transformed to an enrolled featurecorresponding to the new or updated feature extractor based on atransformation model, and the authentication process 152 is performedbased on the new enrolled feature. Hereinafter, the term “old enrolledfeature” or “first enrolled feature” is used in relation to the oldfeature extractor or the first feature extractor, and the term “newenrolled feature” or “second enrolled feature” is used in relation tothe new feature extractor or the second feature extractor and thetransformation model.

FIG. 2 illustrates an example of an authentication process using atransformation model, in accordance with one or more embodiments.

Referring to FIG. 2, a transformation model 210 transforms a firstenrolled feature to a second enrolled feature. The first enrolledfeature refers to an old enrolled feature. The first enrolled featuremay be a feature enrolled using the feature extractor 110 of FIG. 1through the enrollment process 151 of FIG. 1. In this example, the firstenrolled feature is construed as corresponding to the feature extractor110 of FIG. 1, and the second enrolled feature is construed ascorresponding to a feature extractor 220 of FIG. 2. The featureextractor 220 is an updated version of the feature extractor 110 of FIG.1.

An authentication apparatus 200 determines an input feature byextracting a feature from input data using the feature extractor 220.For ease of description, the input feature generated based on thefeature extractor 110 of FIG. 1 is referred to as a first input feature,and the input feature generated based on the feature extractor 220 isreferred to as a second input feature. The authentication apparatus 200performs an authentication based on the second enrolled feature and thesecond input feature. For example, the authentication apparatus 200determines a comparison result by comparing the second enrolled featureand the second input feature using a comparator 230, and generates anauthentication result based on the comparison result and a threshold.

The feature extractor 220 and the comparator 230 are each implementedthrough at least one hardware module, at least one software module, or acombination thereof. For example, the feature extractor 220 and thecomparator 230 may each be implemented as a neural network. Thedescription of the neural network of FIG. 1 applies to the featureextractor 220 and the comparator 230 of FIG. 2.

In a non-limiting example transformation process 251 is performed by theauthentication apparatus 200. In another example, the transformationprocess 251 may be implemented by another apparatus different from theauthentication apparatus 200. For example, the authentication apparatus200 includes the transformation model 210, and transforms the firstenrolled feature to the second enrolled feature using the transformationmodel 210. In another example, instead of the authentication apparatus200, another apparatus may include the transformation model 210. In thisexample, the other apparatus may transform the first enrolled feature tothe second enrolled feature using the transformation model 210, and theauthentication apparatus 200 may then receive the second enrolledfeature to which the first enrolled feature is transformed from theother apparatus. Thus, in an example, the authentication apparatus 200may obtain the second enrolled feature from an external apparatus.

FIG. 3 illustrates an example of an operation of a transformation modelin accordance with one or more embodiments.

Referring to FIG. 3, a transformation model 330 transforms a firstenrolled feature corresponding to a first feature extractor 310 to asecond enrolled feature corresponding to a second feature extractor 320.The first feature extractor 310 and the second feature extractor 320 aredifferent from each other. For example, the second feature extractor 320may be a modification of the first feature extractor 310. In detail, thesecond feature extractor 320 may be an updated version of the firstfeature extractor 310. The transformation model 330 is generated basedon the first feature extractor 310 and the second feature extractor 320.

The transformation model 330 includes structural elements correspondingto differences between a structure of the first feature extractor 310and a structure of the second feature extractor 320, and transforms thefirst enrolled feature to the second enrolled feature using thecorresponding structural elements. In another example, thetransformation model 330 is pretrained to output an output of the secondfeature extractor 320 when an output of the first feature extractor 310is input thereinto. Through the training as described above, thetransformation model 330 outputs the second enrolled feature in responseto an input of the first enrolled feature.

FIG. 4 illustrates an example of generating a transformation model.Referring to FIG. 4, a feature extractor 410 includes a plurality oflayers 415, and a feature extractor 420 includes the plurality of layers415 and a plurality of layers 425. In an example, the plurality oflayers 415 and 425 correspond to convolutional layers. The featureextractor 410 corresponds to a first feature extractor, and the featureextractor 420 corresponds to a second feature extractor.

For example, the plurality of layers 425 is added to the second featureextractor 420 during a process of updating the first feature extractor410 to the second feature extractor 420. Structural elementscorresponding to differences between a structure of the first featureextractor 410 and a structure of the second feature extractor 420 arethe plurality of layers 425. Thus, a transformation model 430 isgenerated based on the plurality of layers 425. For example, thetransformation model 430 is generated to include the plurality of layers425.

The first feature extractor 410 outputs first output data in response toan input of input data, and the second feature extractor 420 outputssecond output data in response to an input of the input data. Thetransformation model 430 outputs the second output data in response toan input of the first output data. The first output data is output basedon the plurality of layers 415, and the second output data is outputbased on a combination of the plurality of layers 415 and the pluralityof layers 425. Thus, the first output data is transformed to the secondoutput data through the plurality of layers 425 of the transformationmodel 430.

The first output data and the second output data correspond to the firstenrolled feature and the second enrolled feature of FIG. 3. Thus, thetransformation model 430 transforms the first enrolled feature to thesecond enrolled feature based on the structural elements correspondingto the differences between the structure of the first feature extractor410 and the structure of the second feature extractor 420.

FIG. 5 illustrates an example of generating a transformation model.Referring to FIG. 5, a transformation model 530 is trained based onrespective outputs of a feature extractor 510 and a feature extractor520 with respect to the same input. The feature extractor 510corresponds to a first feature extractor, and the feature extractor 520corresponds to a second feature extractor. For example, the firstfeature extractor 510 outputs first output data in response to an inputof input data, and the second feature extractor 520 outputs secondoutput data in response to an input of the same input data. Thetransformation model 530 is trained to output the second output data inresponse to an input of the first output data. The first output data andthe second output data correspond to the first enrolled feature and thesecond enrolled feature of FIG. 3. Thus, the transformation model 530transforms the first enrolled feature to the second enrolled featurebased on training as described above.

FIG. 6 illustrates an example of second sub-enrolled features inaccordance with one or more embodiments. An enrolled feature includessub-enrolled features. The sub-enrolled features may be determinedthrough a single enrollment process, or may be determined through aplurality of enrollment processes. For example, in an example of facerecognition, sub-enrolled features corresponding to respective parts ofa face may be determined based on a single facial image or a pluralityof facial images captured in an enrollment process.

A first enrolled feature includes first sub-enrolled features, and anauthentication apparatus transforms the first sub-enrolled features tosecond sub-enrolled features using a transformation model. The secondsub-enrolled features constitute a second enrolled feature. Theauthentication apparatus determines whether the second sub-enrolledfeatures include an inappropriate sub-enrolled feature in order toprevent a false acceptance. The false acceptance may include theincorrect recognition of a test user who is not a legitimate user asbeing a legitimate user. The authentication apparatus determinessuitabilities or similarities of the second sub-enrolled features to thefirst sub-enrolled features, or similarities between one secondsub-enrolled feature and the remaining second sub-enrolled features, anddiscards at least a portion of the second sub-enrolled features based onthe determined suitabilities or similarities of the second sub-enrolledfeatures to the first sub-enrolled features, or determined similaritiesbetween one second sub-enrolled feature and the remaining secondsub-enrolled features.

In FIG. 6, the indicated points are vectors corresponding to the secondsub-enrolled features represented on a predetermined coordinate plane.The second sub-enrolled features are classified as second sub-enrolledfeatures 610 forming a cluster and as second sub-enrolled features 620and 630 corresponding to outliers. The authentication apparatuscalculates suitabilities or similarities of the second sub-enrolledfeatures 620 and 630 corresponding to the outliers to be relatively low,and configures a second enrolled feature by excluding the secondsub-enrolled features 620 and 630.

The authentication apparatus discards at least a portion of the secondsub-enrolled features based on a similarity of the second sub-enrolledfeatures. For example, the authentication apparatus discards one of thesecond sub-enrolled features based on a threshold, hereinafter,identified as TH2, and similarities between the one second sub-enrolledfeature and the remaining second sub-enrolled features.

A similarity parameter related to each second sub-enrolled feature isdetermined based on Equation 1 below.

$\begin{matrix}{{T(k)} = {\frac{1}{N - 1}{\sum\limits_{{j = 1},{j \neq k}}^{N}{{Similarity}( {b_{k},b_{j}} )}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1, T(k) denotes a similarity parameter related to a secondsub-enrolled feature corresponding to an index k, N denotes a totalnumber of second sub-enrolled features, and Similarity(b_(k), b_(j))denotes a similarity between a second sub-enrolled feature b_(k) and asecond sub-enrolled feature b_(j). For example, Similarity(b_(k), b_(j))is determined based on a distance or a difference between the secondsub-enrolled feature b_(k) and the second sub-enrolled feature b_(j).Similarity(b_(k), b_(j)) has a relatively large value as the distancebetween the second sub-enrolled feature b_(k) and the secondsub-enrolled feature b_(j) decreases. A sum of Similarity(b_(k), b_(j))values is divided by N−1, and thus the similarity parameter is alsoreferred to as a normalized similarity.

The authentication apparatus determines the similarity parameter relatedto each second sub-enrolled feature based on Equation 1, and discards atleast a portion of the second sub-enrolled features based on thethreshold TH2 and the similarity parameter. For example, theauthentication apparatus excludes a second sub-enrolled feature having asimilarity parameter less than the threshold TH2.

In an example, the threshold TH2 is equal to a threshold, hereinafter,identified as TH1, used in an authentication process. For example, theauthentication apparatus performs an authentication based on thethreshold TH1 and a similarity between a second enrolled feature and asecond input feature. The second enrolled feature is an enrolled featureto which the first enrolled feature is transformed by a transformationmodel, and the second input feature is extracted from input data using asecond feature extractor. However, examples are not limited thereto. Thethreshold TH2 may have various values different from the threshold TH1.

A transformation process is performed by various devices. FIGS. 7through 10 illustrate various examples of a transformation operation. Atransformation of an enrolled feature may also be implemented throughvarious examples other than the examples set forth hereinafter withreference to FIGS. 7 through 10. A user terminal 700 of FIG. 7, a userterminal 820 of FIG. 8, Internet of things (IoT) terminals 920, 930, and940 of FIG. 9, and IoT terminals 1020,1030, and 1040 of FIG. 10 eachinclude the authentication apparatus described with reference to FIGS. 1through 6, or perform the functions of the authentication apparatusdescribed with reference to FIGS. 1 through 6.

FIG. 7 illustrates an example of a transformation operation of a userterminal in accordance with one or more embodiments.

Referring to FIG. 7, the user terminal 700 includes a transformationmodel 750. The user terminal 700 transforms a first enrolled feature toa second enrolled feature using the transformation model 750. Thetransformation model 750 may be generated by the user terminal 700, ormay be generated by another apparatus different from the user terminal700, for example, a server. The user terminal 700 performs anauthentication process based on the second enrolled feature.

FIG. 8 illustrates an example of a transformation operation that isperformed through a server in accordance with one or more embodiments.

Referring to FIG. 8, a server 810 includes a transformation model 815,and the user terminal 820 transforms a first enrolled feature to asecond enrolled feature using the transformation model 815 in the server810. For example, the user terminal 820 transmits the first enrolledfeature to the server 810, and the server 810 transforms the firstenrolled feature to the second enrolled feature using the transformationmodel 815 and transmits the second enrolled feature to the user terminal820. The transformation model 815 is generated by the server 810. Theuser terminal 820 performs an authentication process based on the secondenrolled feature.

FIG. 9 illustrates an example of a transformation operation that isperformed through a hub device in an IoT system in accordance with oneor more embodiments.

Referring to FIG. 9, a hub device 910 includes a transformation model915, and the IoT terminals 920, 930, and 940. The IoT terminals 920,930, and 940 each transform an old enrolled feature to a new enrolledfeature using the transformation model 915 in the hub device 910. Forexample, the IoT terminals 920, 930, and 940 transmit the old enrolledfeatures to the hub device 910, and the hub device 910 transforms theold enrolled features to new enrolled features using the transformationmodel 915 and transmits the new enrolled features to the respective IoTterminals 920, 930, and 940. The transformation model 915 may begenerated by the hub device 910, or may be generated by anotherapparatus different from the hub device 910, for example, a server. TheIoT terminals 920, 930, and 940 may each perform an authenticationprocess based on the corresponding new enrolled feature.

FIG. 10 illustrates an example of a transformation operation through ahub device in an IoT system in accordance with one or more embodiments.

Referring to FIG. 10, a hub device 1010 includes a transformation model1015. The transformation model 1015 includes, as non-limited examples, afirst sub-transformation model 1016, a second sub-transformation model1017, and a third sub-transformation model 1018. The firstsub-transformation model 1016 transforms a first enrolled feature to asecond enrolled feature, the second sub-transformation model 1017transforms the first enrolled feature to a third enrolled feature, andthe third sub-transformation model 1018 transforms the first enrolledfeature to a fourth enrolled feature.

The first enrolled feature corresponds to a first feature extractor usedby the hub device 1010, the second enrolled feature corresponds to asecond feature extractor used by the IoT terminal 1020, the thirdenrolled feature corresponds to a third feature extractor used by theIoT terminal 1030, and the fourth enrolled feature corresponds to afourth feature extractor used by the IoT terminal 1040. In an example,the first feature extractor may also be used by another representationdevice of the IoT system. The first sub-transformation model 1016 isgenerated based on the first feature extractor and the second featureextractor, the second sub-transformation model 1017 is generated basedon the first feature extractor and the third feature extractor, and thethird sub-transformation model 1018 is generated based on the firstfeature extractor and the fourth feature extractor. Contrary to thedefinitions provided above, in FIG. 10, the first enrolled feature isreferred to as a source enrolled feature which is a feature enrolledthrough the hub device 1010 or a representative device, and the secondenrolled feature, the third enrolled feature, and the fourth enrolledfeature are referred to as individual enrolled features which areenrolled features used respectively by the IoT terminals 1020, 1030, and1040.

The hub device 1010 separately transforms the first enrolled feature tothe second enrolled feature, the third enrolled feature, and the fourthenrolled feature using the transformation model 1015, and respectivelytransmits the second enrolled feature, the third enrolled feature, andthe fourth enrolled feature to the respective IoT terminals 1010, 1020,and 1030. The transformation model 1015 may be generated by the hubdevice 1010, or may be generated by another apparatus different from thehub device 1010, for example, a server. The IoT terminals 1010, 1020,and 1030 perform authentication processes based on the second enrolledfeature, the third enrolled feature, and the fourth enrolled feature.

FIG. 11 illustrates an example of a configuration of an authenticationapparatus in accordance with one or more embodiments.

Referring to FIG. 11, an authentication apparatus 1100 includes aprocessor 1110 and a memory 1120. In an example, the authenticationapparatus 1100 may further store instructions, e.g., in memory 1120,which when executed by the processor 1110 configure the processor 1110to implement one or more or any combination of operations herein. Theprocessor 1110 and the memory 1120 may be respectively representative ofone or more processors 1110 and one or more memories 120.

The authentication apparatus 1100 performs the at least one operationdescribed or illustrated herein in relation to an authentication, andprovides an authentication result to a user. The memory 1120 isconnected to the processor 1110, and stores instructions executable bythe processor 1110, data to be calculated by the processor 1110, or dataprocessed by the processor 1110. The memory 1120 includes anon-transitory computer readable medium, for example, a high-speedrandom access memory, and/or a non-volatile computer readable storagemedium, for example, at least one disk storage device, flash memorydevice, or other non-volatile solid state memory devices.

The processor 1110 executes instructions to perform the at least oneoperation described with reference to FIGS. 1 through 10. For example,the processor 1110 obtains a second enrolled feature to which a firstenrolled feature generated based on a first feature extractor istransformed, determines an input feature by extracting a feature frominput data using a second feature extractor different from the firstfeature extractor, and performs an authentication based on the secondenrolled feature and the input feature.

FIG. 12 illustrates an example of an authentication method in accordancewith one or more embodiments. The operations in FIG. 12 may be performedin the sequence and manner as shown, although the order of someoperations may be changed or some of the operations omitted withoutdeparting from the spirit and scope of the illustrative examplesdescribed. Many of the operations shown in FIG. 12 may be performed inparallel or concurrently. One or more blocks of FIG. 12, andcombinations of the blocks, can be implemented by special purposehardware-based computer that perform the specified functions, orcombinations of special purpose hardware and computer instructions. Inaddition to the description of FIG. 12 below, the descriptions of FIGS.1-11 are also applicable to FIG. 12, and are incorporated herein byreference. Thus, the above description may not be repeated here.

Referring to FIG. 12, in operation 1210, an authentication apparatusobtains a second enrolled feature to which a first enrolled featuregenerated based on a first feature extractor is transformed. Inoperation 1220, the authentication apparatus determines an input featureby extracting a feature from input data using a second feature extractordifferent from the first feature extractor. In operation 1230, theauthentication apparatus discards at least a portion of secondsub-enrolled features included in the second enrolled feature based onsuitabilities or similarities of the second sub-enrolled features. Inoperation 1240, the authentication apparatus performs an authenticationbased on the second enrolled feature and the input feature. In addition,the description provided with reference to FIGS. 1 through 10 applies tothe authentication method.

The authentication apparatuses 100, 200, and 1100, the feature extractor110, the comparator 120, transformation model 210, feature extractor220, comparator 230, first extractor 310, second extractor 320,transformation model 330, feature extractor 410, feature 420,transformation model 430, first feature extractor 510, second featureextractor 520, transformation model 530, user terminal 700,transformation model 750, server 810, transformation model 15, userterminal 820, hub device 910, transformation 915, IoT terminals 920,930, and 940, hub device 1010, IoT terminals 1029, 1030, and 1040,authentication apparatus 1100, processor 1110, and memory 1120, andother apparatuses, units, modules, devices, and other componentsdescribed herein with respect to FIGS. 1-12 are implemented as and byhardware components. Examples of hardware components that may be used toperform the operations described in this application where appropriateinclude controllers, sensors, generators, drivers, memories,comparators, arithmetic logic units, adders, subtractors, multipliers,dividers, integrators, and any other electronic components configured toperform the operations described in this application. In other examples,one or more of the hardware components that perform the operationsdescribed in this application are implemented by computing hardware, forexample, by one or more processors or computers. A processor or computermay be implemented by one or more processing elements, such as an arrayof logic gates, a controller and an arithmetic logic unit, a digitalsignal processor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-12 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control a processor or computer to implementthe hardware components and perform the methods as described above arewritten as computer programs, code segments, instructions or anycombination thereof, for individually or collectively instructing orconfiguring the processor or computer to operate as a machine orspecial-purpose computer to perform the operations performed by thehardware components and the methods as described above. In one example,the instructions or software include machine code that is directlyexecuted by the processor or computer, such as machine code produced bya compiler. In another example, the instructions or software includehigher-level code that is executed by the processor or computer using aninterpreter. Programmers of ordinary skill in the art can readily writethe instructions or software based on the block diagrams and the flowcharts illustrated in the drawings and the corresponding descriptions inthe specification, which disclose algorithms for performing theoperations performed by the hardware components and the methods asdescribed above.

The instructions or software to control a processor or computer toimplement the hardware components and perform the methods as describedabove, and any associated data, data files, and data structures, arerecorded, stored, or fixed in or on one or more non-transitorycomputer-readable storage media. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access programmable read only memory (PROM), electricallyerasable programmable read-only memory (EEPROM), random-access memory(RAM), dynamic random access memory (DRAM), static random access memory(SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs,CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs,BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage,hard disk drive (HDD), solid state drive (SSD), flash memory, a cardtype memory such as multimedia card micro or a card (for example, securedigital (SD) or extreme digital (XD)), magnetic tapes, floppy disks,magneto-optical data storage devices, optical data storage devices, harddisks, solid-state disks, and any other device that is configured tostore the instructions or software and any associated data, data files,and data structures in a non-transitory manner and providing theinstructions or software and any associated data, data files, and datastructures to a processor or computer so that the processor or computercan execute the instructions.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner, and/or replaced or supplemented by othercomponents or their equivalents. Therefore, the scope of the disclosureis defined not by the detailed description, but by the claims and theirequivalents, and all variations within the scope of the claims and theirequivalents are to be construed as being included in the disclosure.

What is claimed is:
 1. A processor implemented authentication method,comprising: obtaining a second enrolled feature to which a firstenrolled feature generated based on a first feature extractor istransformed; determining an input feature by extracting a feature frominput data with a second feature extractor different from the firstfeature extractor; and performing an authentication based on the secondenrolled feature and the input feature.
 2. The method of claim 1,wherein the obtaining comprises transforming the first enrolled featureto the second enrolled feature with a transformation model.
 3. Themethod of claim 1, wherein the obtaining comprises receiving, fromanother apparatus, the second enrolled feature to which the firstenrolled feature is transformed.
 4. The method of claim 1, wherein thesecond feature extractor is an updated version of the first featureextractor.
 5. The method of claim 1, wherein the second enrolled featureis obtained based on a transformation model.
 6. The method of claim 5,wherein the transformation model includes a structural element thatcorresponds to a difference between a structure of the first featureextractor and a structure of the second feature extractor.
 7. The methodof claim 5, wherein the first feature extractor is pretrained to outputfirst output data in response to an input of first input data, thesecond feature extractor is pretrained to output second output data inresponse to an input of the first input data, and the transformationmodel is pretrained to output the second output data in response to aninput of the first output data.
 8. The method of claim 1, wherein thefirst enrolled feature includes first sub-enrolled features, and thesecond enrolled feature includes second sub-enrolled features to whichthe first sub-enrolled features are transformed.
 9. The method of claim8, further comprising: discarding at least a portion of the secondsub-enrolled features based on suitabilities of the second sub-enrolledfeatures.
 10. The method of claim 8, further comprising: discarding atleast a portion of the second sub-enrolled features based on asimilarity between the second sub-enrolled features.
 11. The method ofclaim 9, wherein the discarding comprises discarding at least one of thesecond sub-enrolled features based on a second threshold andsimilarities between the at least one second sub-enrolled feature andremaining second sub-enrolled features.
 12. The method of claim 11,wherein the performing comprises performing the authentication based ona first threshold and a similarity between the second enrolled featureand the input feature, wherein the first threshold is equal to thesecond threshold.
 13. A non-transitory computer-readable storage mediumstoring instructions that, when executed by a processor, cause theprocessor to perform the machine learning based authentication method ofclaim
 1. 14. An authentication apparatus comprising: one or moreprocessors configured to: obtain a second enrolled feature to which afirst enrolled feature generated based on a first feature extractor istransformed; determine an input feature by extracting a feature frominput data with a second feature extractor different from the firstfeature extractor; and perform an authentication based on the secondenrolled feature and the input feature.
 15. The apparatus of claim 14,wherein the second enrolled feature is obtained based on atransformation model.
 16. The apparatus of claim 15, wherein thetransformation model includes a structural element that corresponds to adifference between a structure of the first feature extractor and astructure of the second feature extractor.
 17. The apparatus of claim15, wherein the first feature extractor is pretrained to output firstoutput data in response to an input of first input data, the secondfeature extractor is pretrained to output second output data in responseto an input of the first input data, and the transformation model ispretrained to output the second output data in response to an input ofthe first output data.
 18. The apparatus of claim 14, wherein the firstenrolled feature includes first sub-enrolled features, and the secondenrolled feature includes second sub-enrolled features to which thefirst sub-enrolled features are transformed.
 19. The apparatus of claim18, wherein the one or more processors are configured to discard atleast a portion of the second sub-enrolled features based on asimilarity between the second sub-enrolled features.
 20. The apparatusof claim 19, wherein the one or more processors are configured todiscard one of the second sub-enrolled features based on a threshold andsimilarities between the one second sub-enrolled feature and theremaining second sub-enrolled features.
 21. The apparatus of claim 14,further comprising a memory storing instructions that, when executed bythe one or more processors, configure the one or more processors toperform the generating of the first enrolled feature, the obtaining ofthe second enrolled feature, the determining of the input feature, andthe performing of the authentication.